Tracking a moving object is an important function for home service robots. For example, a robot can follow the owner to provide necessary assistance, or the robot can follow a suspect for home security reasons.
Tracking objects in videos has been actively studied in the field for the past decades, such as tracking cars in surveillance videos for traffic monitoring, or tracking people in surveillance videos for security monitoring. In these previous works, the cameras are either static or moving passively. That is, the camera movement is not controlled by the object tracking methods. For instance, in surveillance videos, the cameras are normally fixed, and the camera motion shown in the video is mostly from camera shaking for a variety of reasons.
Tracking a moving object for home service robots is quite different from the above object tracking tasks in videos. The robot is a moving platform that needs to navigate through a dynamic environment. In other words, besides tracking a target object, the robot actively decides how to move to track the moving object, and at the same time, to navigate through an environment that is either known or unknown, and to avoid other obstacles (such as other moving or static objects) in the environment.
A Simultaneous Localization and Mapping (SLAM) technology enables a robot to navigate through a known or unknown environment. In SLAM, the robot builds and maintains a 2D/3D map of a known or unknown environment and at the same time, localizes (determines its location) itself in the built environment map. Other techniques, such as reinforcement learning (RL) have also been used to facilitate robots in navigation and obstacle avoidance in pre-built environment maps with dynamic small-scale changing components such as static or moving obstacles.